def test_get_statistic_multiple_equals_get_statistic(self): N = 10 X = np.random.randn(N) me = GaussianQuadraticTest(self.grad_log_normal) U_matrix_multiple, stat_multiple = me.get_statistic_multiple(X) U_matrix, stat = me.get_statisitc(N, X) assert_allclose(stat, stat_multiple) assert_allclose(U_matrix_multiple, U_matrix)
import numpy as np def grad_log_normal(x): return -x np.random.seed(42) me = GaussianQuadraticTest(grad_log_normal) res = np.empty((0, 2)) for i in range(50): data = np.random.randn(75) _, s1 = me.get_statisitc(len(data), data) res = np.vstack((res, np.array([75, s1]))) for i in range(50): data = np.random.randn(100) _, s1 = me.get_statisitc(len(data), data) res = np.vstack((res, np.array([100, s1]))) for i in range(50): data = np.random.randn(150) _, s1 = me.get_statisitc(len(data), data) res = np.vstack((res, np.array([150, s1]))) df = DataFrame(res) pr = seaborn.boxplot(x=0, y=1, data=df) seaborn.plt.show()
def grad_log_normal(x): return -x np.random.seed(42) me = GaussianQuadraticTest(grad_log_normal) res = np.empty((0,2)) for i in range(50): data = np.random.randn(75) _,s1 = me.get_statisitc(len(data),data) res = np.vstack((res,np.array([75, s1]))) for i in range(50): data = np.random.randn(100) _,s1 = me.get_statisitc(len(data),data) res = np.vstack((res,np.array([100, s1]))) for i in range(50): data = np.random.randn(150) _,s1 = me.get_statisitc(len(data),data) res = np.vstack((res,np.array([150, s1])))